Interpretable scRNA-seq Trajectory DE with {scLANE}

Author
Affiliation

Jack Leary

University of Florida

Published

June 1, 2023

Introduction

In this tutorial we’ll walk through a basic trajectory differential expression analysis. We’ll use the {scLANE} package, which we developed with the goal of providing accurate and biologically interpretable models of expression over the course of a biological process. At the end are a list of references we used in developing the method & writing the accompanying manuscript, as well as the poster I presented at ENAR 2023 in Nashville.

Libraries

If you haven’t already, install the development version (currently v0.6.2) of {scLANE} from the GitHub repository.

Code
remotes::install_github("jr-leary7/scLANE")

Next, we’ll load the packages we need to process, analyze, & visualize our data.

Code
library(dplyr)           # data manipulation
library(scLANE)          # trajectory DE 
library(Seurat)          # scRNA-seq tools
library(ggplot2)         # plot utilities 
library(patchwork)       # plot combination
library(slingshot)       # pseudotime estimation
library(reticulate)      # Python interface
library(ComplexHeatmap)  # heatmaps
rename <- dplyr::rename

Helper Functions

We’ll also define a couple utilities to make our plots cleaner to read & easier to make.

Code
theme_umap <- function(base.size = 14) {
  ggplot2::theme_classic(base_size = base.size) + 
  ggplot2::theme(axis.ticks = ggplot2::element_blank(), 
                 axis.text = ggplot2::element_blank(), 
                 plot.subtitle = ggplot2::element_text(face = "italic", size = 11), 
                 plot.caption = ggplot2::element_text(face = "italic", size = 11))
}
guide_umap <- function(key.size = 4) {
  ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = key.size, alpha = 1)))
}

And consistent color palettes will make our plots easier to understand.

Code
palette_cluster <- paletteer::paletteer_d("ggsci::default_jama")
palette_celltype <- paletteer::paletteer_d("ggsci::category20_d3")
palette_heatmap <- paletteer::paletteer_d("wesanderson::Zissou1")

Data

We’ll load the pancreatic endocrinogenesis data from Bastidas-Ponce et al (2019), which comes with the scVelo Python library & has been used in several pseudotime inference / RNA velocity method papers as a good benchmark dataset.

Code
import scvelo as scv
adata = scv.datasets.pancreas()

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The AnnData object contains data that we’ll need to extract, specifically the counts matrices (stored in AnnData.layers) and the cell-level metadata (which is in AnnData.obs).

Code
adata
AnnData object with n_obs × n_vars = 3696 × 27998
    obs: 'clusters_coarse', 'clusters', 'S_score', 'G2M_score'
    var: 'highly_variable_genes'
    uns: 'clusters_coarse_colors', 'clusters_colors', 'day_colors', 'neighbors', 'pca'
    obsm: 'X_pca', 'X_umap'
    layers: 'spliced', 'unspliced'
    obsp: 'distances', 'connectivities'

The {reticulate} package allows us to pass the counts matrices & metadata from Python back to R. We’ll use the spliced mRNA counts as our default assay, and also define a new assay containing the total (spliced + unspliced) mRNA in each cell. Lastly, we remove genes with non-zero spliced mRNA in 3 or fewer cells. Note: while downloading this dataset requires a Python installation as well as the installation of the scVelo Python library (and its dependencies), running {scLANE} is done purely in R & requires no Python whatsoever.

Code
spliced_counts <- Matrix::Matrix(t(as.matrix(py$adata$layers["spliced"])), sparse = TRUE)
unspliced_counts <- Matrix::Matrix(t(as.matrix(py$adata$layers["unspliced"])), sparse = TRUE)
rna_counts <- spliced_counts + unspliced_counts
colnames(rna_counts) <- colnames(spliced_counts) <- colnames(unspliced_counts) <- py$adata$obs_names$to_list()
rownames(rna_counts) <- rownames(spliced_counts) <- rownames(unspliced_counts) <- py$adata$var_names$to_list()
spliced_assay <- CreateAssayObject(counts = spliced_counts)
spliced_assay@key <- "spliced_"
unspliced_assay <- CreateAssayObject(counts = unspliced_counts)
unspliced_assay@key <- "unspliced_"
rna_assay <- CreateAssayObject(counts = rna_counts)
rna_assay@key <- "rna_"
meta_data <- py$adata$obs %>% 
             mutate(cell_name = rownames(.), .before = 1) %>% 
             rename(celltype = clusters, 
                    celltype_coarse = clusters_coarse) %>% 
             mutate(nCount_spliced = colSums(spliced_counts), 
                    nFeature_spliced = colSums(spliced_counts > 0), 
                    nCount_unspliced = colSums(unspliced_counts), 
                    nFeature_unspliced = colSums(unspliced_counts > 0), 
                    nCount_rna = colSums(rna_counts), 
                    nFeature_rna = colSums(rna_counts > 0))
seu <- CreateSeuratObject(counts = spliced_assay, 
                          assay = "spliced", 
                          project = "Mm_Panc_Endo", 
                          meta.data = meta_data)
seu@assays$unspliced <- unspliced_assay
seu@assays$rna <- rna_assay
seu <- seu[rowSums(seu@assays$spliced) > 3, ]

We preprocess the counts using a typical pipeline with QC, normalization, linear & nonlinear dimension reduction, and graph-based clustering via the Leiden algorithm.

Code
seu <- PercentageFeatureSet(seu, 
                            pattern = "^mt-", 
                            col.name = "percent_mito", 
                            assay = "spliced") %>% 
       PercentageFeatureSet(pattern = "^Rp[sl]", 
                            col.name = "percent_ribo", 
                            assay = "spliced") %>% 
       NormalizeData(assay = "spliced", verbose = FALSE) %>% 
       NormalizeData(assay = "unspliced", verbose = FALSE) %>% 
       NormalizeData(assay = "rna", verbose = FALSE) %>% 
       FindVariableFeatures(assay = "spliced", 
                            nfeatures = 3000, 
                            verbose = FALSE) %>% 
       ScaleData(assay = "spliced", 
                 vars.to.regress = c("percent_mito", "percent_ribo"), 
                 model.use = "poisson", 
                 verbose = FALSE) %>% 
       RunPCA(assay = "spliced", 
              npcs = 30, 
              approx = TRUE, 
              seed.use = 312, 
              verbose = FALSE) %>% 
       RunUMAP(reduction = "pca", 
               dims = 1:30, 
               n.components = 2, 
               metric = "cosine", 
               seed.use = 312, 
               verbose = FALSE) %>% 
       FindNeighbors(reduction = "pca", 
                     k.param = 30,
                     nn.method = "annoy", 
                     annoy.metric = "cosine", 
                     verbose = FALSE) %>% 
       FindClusters(algorithm = 4, 
                    resolution = 0.5, 
                    random.seed = 312, 
                    verbose = FALSE)

Let’s visualize the results on our UMAP embedding. The clustering generally agrees with the celltype labels, though there is some overclustering in the ductal cells & underclustering in the mature endocrine celltypes.

Code
p0 <- DimPlot(seu, 
              group.by = "seurat_clusters", 
              pt.size = 1, 
              cols = alpha(palette_cluster, 0.75)) + 
      labs(x = "UMAP 1", 
           y = "UMAP 2", 
           color = "Leiden Cluster") + 
      theme_umap() + 
      theme(plot.title = element_blank()) + 
      guide_umap()
p1 <- DimPlot(seu, 
              group.by = "celltype", 
              pt.size = 1, 
              cols = alpha(palette_celltype, 0.75)) + 
      labs(x = "UMAP 1", 
           y = "UMAP 2", 
           color = "Celltype") + 
      theme_umap() + 
      theme(plot.title = element_blank()) + 
      guide_umap()
p2 <- (p0 / p1) + 
      plot_annotation(title = "Murine Pancreatic Endocrinogenesis", 
                      theme = theme_classic(base_size = 14))
p2

Trajectory Inference

Pseudotime Estimation

We’ll start by fitting a trajectory using the {slingshot} R package. We define cluster 4 as the starting cluster, since in this case we’re already aware of the dataset’s underlying biology. After generating the estimates for each cell, we rescale the ordering to be defined on \([0, 1]\). This has no effect on the trajectory DE results however, and is mostly an aesthetic choice.

Code
sling_res <- slingshot(Embeddings(seu, "umap"), 
                       clusterLabels = seu$seurat_clusters, 
                       start.clus = "4", 
                       approx_points = 1000)
sling_pt <- slingPseudotime(sling_res) %>% 
            as.data.frame() %>% 
            magrittr::set_colnames(c("PT")) %>% 
            mutate(PT = (PT - min(PT)) / (max(PT) - min(PT)))
seu <- AddMetaData(seu, 
                   metadata = sling_pt, 
                   col.name = "sling_pt")

Let’s visualize the results on our UMAP embedding. They match what we would expect (knowing the biological background of the data), with ductal cells at the start of the process and endocrine celltypes such as alpha, beta, & delta cells at the end of it.

Code
p3 <- Embeddings(seu, "umap") %>% 
      as.data.frame() %>% 
      magrittr::set_colnames(c("UMAP_1", "UMAP_2")) %>% 
      mutate(PT = sling_pt$PT) %>% 
      ggplot(aes(x = UMAP_1, y = UMAP_2, color = PT)) + 
      geom_point(size = 1, alpha = 0.75) + 
      labs(x = "UMAP 1", 
           y = "UMAP 2", 
           color = "Pseudotime") + 
      scale_color_gradientn(colors = palette_heatmap, 
                            labels = scales::label_number(accuracy = 0.1)) + 
      theme_umap()
p4 <- (p3 / p1) + 
      plot_annotation(title = "Estimated Cell Ordering from Slingshot", 
                      theme = theme_classic(base_size = 14))
p4

Trajectory Differential Expression

Next, we prepare the primary inputs to {scLANE}: a dense counts matrix (with cells as rows and genes as columns - this is important), a dataframe containing our estimated pseudotime ordering, and a character vector of the genes that we’re interested in modeling. We parallelize over genes in order to speed up the computation at the expense of using a little more memory. The models are fit using NB GLMs with optimal spline knots identified empirically, and differential expression is quantified using a likelihood ratio test of the fitted model vs. the null (intercept-only) model. In practice, genes designated as HVGs are usually the best candidates for modeling, so we choose the top 3,000 HVGs as our input. Note: the testing of the HVG set on its own is also justified by the reality that almost all trajectories are inferred using some sort of dimension-reduced space, and those embeddings are nearly universally generated using a set of HVGs. As such, genes not included in the HVG set actually have no direct relationship with the estimated trajectory, & it’s generally safe to exclude them from trajectory analyses.

Code
top3k_hvg <- HVFInfo(seu) %>% 
             arrange(desc(variance.standardized)) %>% 
             slice_head(n = 3000) %>% 
             rownames(.)
raw_counts <- t(as.matrix(seu@assays$spliced@counts[top3k_hvg, ]))
scLANE_res <- testDynamic(expr.mat = raw_counts, 
                          pt = sling_pt, 
                          n.potential.basis.fns = 4, 
                          parallel.exec = TRUE, 
                          n.cores = 5, 
                          track.time = TRUE)
[1] "testDynamic evaluated 3000 genes with 1 lineage apiece in 12.507 mins"

We pull a sample of 10 genes from the results (which we clean up using getResultsDE()) & display their test statistics. By default, any gene with an adjusted p-value less than 0.01 is predicted to be dynamic, though this threshold can be easily adjusted.

Code
scLANE_res_tidy <- getResultsDE(test.dyn.results = scLANE_res)
set.seed(629)
select(scLANE_res_tidy, 
       Gene, 
       Test_Stat, 
       P_Val, 
       P_Val_Adj,
       Gene_Dynamic_Overall) %>% 
  mutate(Gene_Dynamic_Overall = if_else(Gene_Dynamic_Overall == 1, "Dynamic", "Static")) %>% 
  slice_sample(n = 10) %>% 
  kableExtra::kbl(digits = 5, 
                  booktabs = TRUE, 
                  col.names = c("Gene", "LRT Statistic", "P-value", "Adj. P-value", "Predicted Gene Status")) %>% 
  kableExtra::kable_classic(full_width = FALSE, "hover")
Gene LRT Statistic P-value Adj. P-value Predicted Gene Status
Cdt1 683.50332 0.00000 0.00000 Dynamic
Plet1 51.35960 0.00000 0.00000 Dynamic
Rnf130 687.54249 0.00000 0.00000 Dynamic
Cib2 325.64929 0.00000 0.00000 Dynamic
Gpx1 1754.86938 0.00000 0.00000 Dynamic
Gm15440 7.48582 0.00622 1.00000 Static
Suclg2 797.59131 0.00000 0.00000 Dynamic
Sox5 239.85120 0.00000 0.00000 Dynamic
Ctsk 14.86037 0.00059 0.23782 Static
Mpp2 130.12658 0.00000 0.00000 Dynamic

Next, we can use the plotModels() function to visualize the fitted models from {scLANE} and compare them to other modeling methods. The gene Neurog3 is strongly associated with epithelial cell differentiation, and indeed we see a very clear, nonlinear transcriptional dynamic across pseudotime for that gene. A traditional GLM fails to capture that nonlinearity, and a GAM over-smooths the trend and does not accurately model the sharpness of the transcriptional switch that occurs halfway through the trajectory. Only the scLANE model accurately models the rapid upregulation and equally swift downregulation of Neurog3 over pseudotime thanks to its adaptive choice of knots & piecewise linear nature.

Code
p5 <- plotModels(scLANE_res, 
                 gene = "Neurog3", 
                 pt = sling_pt, 
                 plot.null = FALSE, 
                 gene.counts = raw_counts) + 
        scale_color_manual(values = c("forestgreen"))
p5

We can check out the actual regression output for our gene of interest as well. The estimated knot is placed at 0.4386.

Code
scLANE_res$Neurog3$Lineage_A$MARGE_Summary %>% 
  mutate(term = gsub("B_final", "", term), 
         term = if_else(term == "Intercept", term, paste0("h", term))) %>% 
  kableExtra::kbl(digits = 3, 
                  booktabs = TRUE, 
                  caption = "scLANE Model Output for <i>Neurog3<\\i>", 
                  col.names = c("Hinge Function", "Coefficient", "Std. Error", "T-statistic", "P-value")) %>% 
  kableExtra::kable_classic(full_width = FALSE, "hover")
scLANE Model Output for Neurog3
Hinge Function Coefficient Std. Error T-statistic P-value
Intercept 3.359 0.073 46.046 0
h(Lineage_A-0.4386) -8.473 0.221 -38.422 0
h(0.4386-Lineage_A) -8.272 0.291 -28.418 0

Using the getFittedValues() function allows us to generate predictions from the models we fit, which we then use to visualize the dynamics of a few genes that are known to be strongly associated with the differentiation of immature cells into mature endocrine phenotypes. For all four genes, the fitted models show knots chosen in the area of pseudotime around the pre-endocrine cells. This tells us that these driver genes are being upregulated in precursor celltypes & are driving differentiation into the mature celltypes such as alpha & beta cells, after which the genes are downregulated.

Code
p6 <- getFittedValues(test.dyn.res = scLANE_res, 
                      genes = c("Chga", "Chgb", "Fev", "Cck"), 
                      pt = sling_pt, 
                      expr.mat = raw_counts, 
                      cell.meta.data = select(seu@meta.data, celltype, celltype_coarse)) %>% 
      ggplot(aes(x = pt, y = expression)) + 
      facet_wrap(~gene, 
                 ncol = 2, 
                 scales = "free_y") + 
      geom_point(aes(color = celltype), size = 1, alpha = 0.75) + 
      geom_vline(data = data.frame(gene = "Chga", knot = unique(scLANE_res$Chga$Lineage_A$MARGE_Slope_Data$Breakpoint)), 
                 mapping = aes(xintercept = knot), 
                 linetype = "dashed", 
                 color = "grey20") + 
      geom_vline(data = data.frame(gene = "Chgb", knot = unique(scLANE_res$Chgb$Lineage_A$MARGE_Slope_Data$Breakpoint)), 
                 mapping = aes(xintercept = knot), 
                 linetype = "dashed", 
                 color = "grey20") + 
      geom_vline(data = data.frame(gene = "Cck", knot = unique(scLANE_res$Cck$Lineage_A$MARGE_Slope_Data$Breakpoint)), 
                 mapping = aes(xintercept = knot), 
                 linetype = "dashed", 
                 color = "grey20") + 
      geom_vline(data = data.frame(gene = "Fev", knot = unique(scLANE_res$Fev$Lineage_A$MARGE_Slope_Data$Breakpoint)), 
                 mapping = aes(xintercept = knot), 
                 linetype = "dashed", 
                 color = "grey20") + 
      geom_ribbon(aes(ymin = scLANE_ci_ll, ymax = scLANE_ci_ul), 
                  size = 0, 
                  fill = "grey", 
                  alpha = 1) + 
      geom_line(aes(y = scLANE_fitted), 
                color = "black", 
                size = 0.75) + 
      scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) + 
      scale_color_manual(values = palette_celltype) + 
      labs(x = "Pseudotime", 
           y = "Expression", 
           title = "Endrocrinogenesis Driver Genes Across Pseudotime", 
           subtitle = "scLANE piecewise negative binomial GLMs") + 
      theme_classic(base_size = 14) + 
      theme(legend.title = element_blank(), 
            strip.text.x = element_text(face = "bold"), 
            plot.subtitle = element_text(face = "italic", size = 11)) + 
      guide_umap()
p6

On the other hand, if we use additive models the “peak” of expression is placed among the mature endocrine celltypes - which doesn’t make biological sense if we know that these genes are driving that process of differentiation. This can of course be tweaked by changing the degree or degrees of freedom of the underlying basis spline, but choosing a “best” value for those hyperparameters can be difficult, whereas scLANE identifies optimal parameters internally by default.

Code
p7 <- getFittedValues(test.dyn.res = scLANE_res, 
                      genes = c("Chga", "Chgb", "Fev", "Cck"), 
                      pt = sling_pt, 
                      expr.mat = raw_counts, 
                      cell.meta.data = select(seu@meta.data, celltype, celltype_coarse)) %>% 
        with_groups(gene, 
                    mutate, 
                    GAM_fitted_link = predict(gamlss::gamlss(expression ~ splines::bs(pt, degree = 3), 
                                                             family = "NBI", 
                                                             control = gamlss::gamlss.control(trace = FALSE))), 
                    GAM_se_link = predict(gamlss::gamlss(expression ~ splines::bs(pt, degree = 3), 
                                                         family = "NBI", 
                                                         control = gamlss::gamlss.control(trace = FALSE)), 
                                          se.fit = TRUE)[[2]]) %>% 
        mutate(GAM_fitted = exp(GAM_fitted_link), 
               GAM_ci_ll = exp(GAM_fitted_link - qnorm(0.975, lower.tail = FALSE) * GAM_se_link), 
               GAM_ci_ul = exp(GAM_fitted_link + qnorm(0.975, lower.tail = FALSE) * GAM_se_link)) %>% 
        ggplot(aes(x = pt, y = expression)) + 
        facet_wrap(~gene, 
                   ncol = 2, 
                   scales = "free_y") + 
        geom_point(aes(color = celltype), size = 1, alpha = 0.75) + 
        geom_ribbon(aes(ymin = GAM_ci_ll, ymax = GAM_ci_ul), 
                    size = 0, 
                    fill = "grey", 
                    alpha = 1) + 
        geom_line(aes(y = GAM_fitted), 
                  color = "black", 
                  size = 0.75) + 
        scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) + 
        scale_color_manual(values = palette_celltype) + 
        labs(x = "Pseudotime", 
             y = "Expression", 
             title = "Endrocrinogenesis Driver Genes Across Pseudotime", 
             subtitle = "Cubic basis spline negative binomial GAMs") + 
        theme_classic(base_size = 14) + 
        theme(legend.title = element_blank(), 
              strip.text.x = element_text(face = "bold"), 
              plot.subtitle = element_text(face = "italic", size = 11)) + 
        guide_umap()
p7

Let’s take a broader view of the dataset by examining the distribution of adaptively chosen knots from our models. We limit the analysis to the set of genes determined to be dynamic.

Code
dyn_genes <- filter(scLANE_res_tidy, Gene_Dynamic_Overall == 1) %>% 
             pull(Gene)
knot_df <- purrr::imap(scLANE_res[dyn_genes], 
                       \(x, y) {
                         data.frame(
                           gene = y, 
                           knot = x$Lineage_A$MARGE_Slope_Data$Breakpoint
                         )
                       }) %>% 
           purrr::reduce(rbind)

We’ll plot a histogram of the knot values along with a ridgeplot of the pseudotime distribution for each celltype. We see that the majority of the selected knots are placed at the beginning of the trajectory, around where the ductal cells transition into endocrine progenitors. A smaller set of knots is placed about halfway through the trajectory, which we’ve annotated as the point at which pre-endocrine cells begin differentiating into mature endocrine phenotypes.

Code
p8 <- ggplot(knot_df, aes(x = knot)) + 
      geom_density(fill = "deepskyblue3", 
                   alpha = 0.75, 
                   color = "deepskyblue4", 
                   size = 1) + 
      scale_x_continuous(limits = c(0, 1), labels = scales::label_number(accuracy = 0.1)) + 
      labs(x = "Knot Location") + 
      theme_classic(base_size = 14) + 
      theme(axis.title.y = element_blank(), 
            axis.text.y = element_blank(), 
            axis.ticks.y = element_blank())
p9 <- data.frame(celltype = seu$celltype, 
                 pt = seu$sling_pt) %>% 
      ggplot(aes(x = pt, y = celltype, fill = celltype, color = celltype)) + 
      ggridges::geom_density_ridges(alpha = 0.75, size = 1) + 
      scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) + 
      scale_fill_manual(values = palette_celltype) + 
      scale_color_manual(values = palette_celltype) + 
      labs(x = "Pseudotime") + 
      theme_classic(base_size = 14) + 
      theme(axis.title.y = element_blank(), 
            legend.title = element_blank()) + 
      guide_umap()
p10 <- (p8 / p9) + 
       plot_layout(heights = c(1, 1.75)) + 
       plot_annotation(title = "Distribution of Adaptively-chosen Knots from scLANE", 
                       theme = theme_classic(base_size = 14))
p10
Picking joint bandwidth of 0.0184

We can extract a matrix of fitted values using smoothedCountsMatrix(); here we focus on the top 1,000 most dynamic genes, with the goal of identifying clusters of similarly-expressed genes. After reducing dimensionality with PCA, we cluster the genes using the Leiden algorithm & embed the genes in two dimensions with UMAP.

Code
smoothed_counts <- smoothedCountsMatrix(scLANE_res, 
                                        genes = dyn_genes[1:2000], 
                                        parallel.exec = TRUE, 
                                        n.cores = 2)
set.seed(312)
smoothed_counts_pca <- irlba::prcomp_irlba(t(smoothed_counts$Lineage_A), 
                                           n = 30, 
                                           center = TRUE, 
                                           scale. = TRUE)
smoothed_counts_umap <- uwot::umap(smoothed_counts_pca$x, 
                                   n_components = 2, 
                                   metric = "cosine", 
                                   n_neighbors = 20, 
                                   init = "spectral")
smoothed_counts_snn <- bluster::makeSNNGraph(smoothed_counts_pca$x, 
                                             k = 20, 
                                             type = "jaccard", 
                                             BNPARAM = BiocNeighbors::AnnoyParam(distance = "Cosine"))
smoothed_counts_clust <- igraph::cluster_leiden(smoothed_counts_snn, 
                                                objective_function = "modularity", 
                                                resolution_parameter = 0.3)
gene_clust_df <- data.frame(gene = colnames(smoothed_counts$Lineage_A), 
                            umap1 = smoothed_counts_umap[, 1], 
                            umap2 = smoothed_counts_umap[, 2], 
                            leiden = as.factor(smoothed_counts_clust$membership - 1L))

The embedding & clustering show that even with the relatively small number of genes, clear patterns are visible.

Code
p11 <- ggplot(gene_clust_df, aes(x = umap1, y = umap2, color = leiden)) + 
       geom_point(size = 1, alpha = 0.75) + 
       labs(x = "UMAP 1", 
            y = "UMAP 2", 
            color = "Leiden Cluster", 
            title = "Unsupervised Clustering of Dynamic Genes", 
            subtitle = "Top 2,000 genes after PCA") +
       paletteer::scale_color_paletteer_d("ggsci::default_igv") + 
       theme_umap() + 
       guide_umap()
p11

We can also plot a heatmap of the dynamic genes; this requires a bit of setup, for which we’ll use the {ComplexHeatmap} package. We scale each gene, and clip values to be on \([-6, 6]\). The columns of the heatmap are ordered by estimated pseudotime.

Code
col_anno_df <- select(seu@meta.data, 
                      cell_name, 
                      celltype, 
                      sling_pt) %>% 
               mutate(celltype = as.factor(celltype)) %>% 
               arrange(sling_pt)
heatmap_mat <- t(scale(smoothed_counts$Lineage_A))
heatmap_mat[heatmap_mat > 6] <- 6
heatmap_mat[heatmap_mat < -6] <- -6
colnames(heatmap_mat) <- seu$cell_name
heatmap_mat <- heatmap_mat[, col_anno_df$cell_name]
palette_celltype_hm <- as.character(palette_celltype[1:length(unique(seu$celltype))])
names(palette_celltype_hm) <- levels(col_anno_df$celltype)
col_anno <- HeatmapAnnotation(Celltype = col_anno_df$celltype, 
                              Pseudotime = col_anno_df$sling_pt, 
                              col = list(Celltype = palette_celltype_hm, 
                                         Pseudotime = circlize::colorRamp2(seq(0, 1, by = 0.25), palette_heatmap)),
                              show_legend = TRUE, 
                              show_annotation_name = FALSE, 
                              gap = unit(1, "mm"), 
                              border = TRUE)
palette_cluster_hm <- as.character(paletteer::paletteer_d("ggsci::default_igv")[1:length(unique(gene_clust_df$leiden))])
names(palette_cluster_hm) <- as.character(unique(gene_clust_df$leiden))
row_anno <- HeatmapAnnotation(Cluster = as.factor(gene_clust_df$leiden), 
                              col = list(Cluster = palette_cluster_hm), 
                              show_legend = TRUE, 
                              show_annotation_name = FALSE, 
                              annotation_legend_param = list(title = "Gene\nCluster"), 
                              gap = unit(1, "mm"), 
                              border = TRUE, 
                              which = "row")

The heatmap shows clear dynamic patterns across pseudotime, and the hierarchical clustering agrees fairly well with our graph-based clustering from earlier.

Code
Heatmap(matrix = heatmap_mat, 
        name = "Spliced\nmRNA", 
        col = circlize::colorRamp2(colors = viridis::inferno(50), 
                                   breaks = seq(min(heatmap_mat), max(heatmap_mat), length.out = 50)), 
        cluster_columns = FALSE,
        show_column_dend = FALSE,
        cluster_column_slices = FALSE,
        width = 12, 
        height = 6, 
        column_title = "Dynamic Genes Across Pseudotime in Murine Pancreatic Endocrinogenesis",
        column_title_gp = gpar(fontface = "bold"), 
        show_row_dend = TRUE,
        top_annotation = col_anno, 
        left_annotation = row_anno, 
        show_column_names = FALSE, 
        show_row_names = FALSE, 
        use_raster = TRUE,
        raster_by_magick = TRUE, 
        raster_quality = 5)
Loading required namespace: magick

Using our gene clusters & the {gprofiler2} package, we run an enrichment analysis against the biological process (BP) set of gene ontologies.

Code
gene_clust_list <- purrr::map(unique(gene_clust_df$leiden), \(x) filter(gene_clust_df, leiden == x) %>% pull(gene)) 
names(gene_clust_list) <- paste0("Leiden_", unique(gene_clust_df$leiden))
enrich_res <- gprofiler2::gost(gene_clust_list, 
                               organism = "mmusculus", 
                               ordered_query = FALSE, 
                               multi_query = FALSE, 
                               sources = "GO:BP", 
                               significant = TRUE)

A look at the top 3 most-significant GO terms for each gene cluster reveals heterogeneous functionalities across groups of genes:

Code
mutate(enrich_res$result, 
       query = gsub("Leiden_", "", query)) %>% 
  rename(cluster = query) %>% 
  with_groups(cluster, 
              slice_head,
              n = 3) %>% 
  select(cluster, term_name, p_value, term_size, query_size, intersection_size, term_id) %>% 
  kableExtra::kbl(digits = 5, 
                  booktabs = TRUE, 
                  caption = "<i>Top 3 Biological Process GO Terms per Cluster<\\i>", 
                  col.names = c("Gene Cluster", "Term Name", "Adj. P-value", "Term Size", 
                                "Query Size", "Intersection Size", "Term ID")) %>% 
  kableExtra::kable_classic(c("hover"), full_width = FALSE)
Top 3 Biological Process GO Terms per Cluster
Gene Cluster Term Name Adj. P-value Term Size Query Size Intersection Size Term ID
0 nervous system development 0.00000 2555 271 80 GO:0007399
0 regulation of secretion by cell 0.00000 673 271 41 GO:1903530
0 amide transport 0.00000 423 271 33 GO:0042886
1 regulation of apoptotic process 0.00000 1633 252 58 GO:0042981
1 regulation of programmed cell death 0.00000 1665 252 58 GO:0043067
1 regulation of cell death 0.00000 1836 252 61 GO:0010941
2 cell-cell signaling 0.00000 1748 308 64 GO:0007267
2 regulation of biological quality 0.00000 3244 308 91 GO:0065008
2 secretion 0.00000 1084 308 48 GO:0046903
3 cell cycle 0.00000 1817 285 174 GO:0007049
3 cell cycle process 0.00000 1242 285 152 GO:0022402
3 mitotic cell cycle 0.00000 881 285 121 GO:0000278
4 animal organ development 0.00000 3326 307 106 GO:0048513
4 system development 0.00000 4115 307 119 GO:0048731
4 developmental process 0.00000 6865 307 158 GO:0032502
5 multicellular organism development 0.00002 4873 145 55 GO:0007275
5 system development 0.00003 4115 145 49 GO:0048731
5 anatomical structure development 0.00014 6228 145 62 GO:0048856
6 anatomical structure development 0.00000 6228 238 111 GO:0048856
6 multicellular organism development 0.00000 4873 238 95 GO:0007275
6 tube development 0.00000 1173 238 43 GO:0035295

Session Info

Code
sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.1 (2022-06-23)
 os       macOS Big Sur ... 10.16
 system   x86_64, darwin17.0
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2023-06-01
 pandoc   2.19.2 @ /usr/local/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 abind                  1.4-5      2016-07-21 [1] CRAN (R 4.2.0)
 assertthat             0.2.1      2019-03-21 [1] CRAN (R 4.2.0)
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 bluster                1.6.0      2022-04-26 [1] Bioconductor
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 Cairo                  1.6-0      2022-07-05 [1] CRAN (R 4.2.0)
 circlize               0.4.15     2022-05-10 [1] CRAN (R 4.2.0)
 cli                    3.3.0      2022-04-25 [1] CRAN (R 4.2.0)
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 cluster                2.1.4      2022-08-22 [1] CRAN (R 4.2.0)
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 codetools              0.2-18     2020-11-04 [1] CRAN (R 4.2.1)
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 ComplexHeatmap       * 2.12.1     2022-08-09 [1] Bioconductor
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 geeM                   0.10.1     2018-06-18 [1] CRAN (R 4.2.0)
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 ggplot2              * 3.3.6      2022-05-03 [1] CRAN (R 4.2.0)
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 globals                0.16.1     2022-08-28 [1] CRAN (R 4.2.1)
 glue                   1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
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 gprofiler2             0.2.1      2021-08-23 [1] CRAN (R 4.2.0)
 gridExtra              2.3        2017-09-09 [1] CRAN (R 4.2.0)
 gtable                 0.3.0      2019-03-25 [1] CRAN (R 4.2.0)
 here                   1.0.1      2020-12-13 [1] CRAN (R 4.2.0)
 highr                  0.9        2021-04-16 [1] CRAN (R 4.2.0)
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 lazyeval               0.2.2      2019-03-15 [1] CRAN (R 4.2.0)
 leiden                 0.4.2      2022-05-09 [1] CRAN (R 4.2.0)
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 Rtsne                  0.16       2022-04-17 [1] CRAN (R 4.2.0)
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 tibble                 3.1.8      2022-07-22 [1] CRAN (R 4.2.0)
 tidyr                  1.2.0      2022-02-01 [1] CRAN (R 4.2.0)
 tidyselect             1.1.2      2022-02-21 [1] CRAN (R 4.2.0)
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 viridisLite            0.4.1      2022-08-22 [1] CRAN (R 4.2.0)
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 withr                  2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
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 xml2                   1.3.3      2021-11-30 [1] CRAN (R 4.2.0)
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 [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library

─ Python configuration ───────────────────────────────────────────────────────
 python:         /Users/jack/Desktop/Python/science/venv/bin/python
 libpython:      /usr/local/opt/python@3.8/Frameworks/Python.framework/Versions/3.8/lib/python3.8/config-3.8-darwin/libpython3.8.dylib
 pythonhome:     /Users/jack/Desktop/Python/science/venv:/Users/jack/Desktop/Python/science/venv
 virtualenv:     /Users/jack/Desktop/Python/science/venv/bin/activate_this.py
 version:        3.8.16 (default, Dec  7 2022, 01:36:11)  [Clang 14.0.0 (clang-1400.0.29.202)]
 numpy:          /Users/jack/Desktop/Python/science/venv/lib/python3.8/site-packages/numpy
 numpy_version:  1.23.5
 
 NOTE: Python version was forced by use_python function

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